---
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swinv2-tiny-patch4-window8-256-finalterm
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.89375
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# swinv2-tiny-patch4-window8-256-finalterm

This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3096
- Accuracy: 0.8938

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3728        | 1.0   | 10   | 1.2644          | 0.5156   |
| 1.1308        | 2.0   | 20   | 0.8816          | 0.625    |
| 0.8721        | 3.0   | 30   | 0.6829          | 0.7063   |
| 0.6919        | 4.0   | 40   | 0.5298          | 0.8063   |
| 0.5876        | 5.0   | 50   | 0.4100          | 0.8688   |
| 0.5504        | 6.0   | 60   | 0.4153          | 0.8531   |
| 0.459         | 7.0   | 70   | 0.3828          | 0.8594   |
| 0.4501        | 8.0   | 80   | 0.3941          | 0.8625   |
| 0.4312        | 9.0   | 90   | 0.3650          | 0.8719   |
| 0.4119        | 10.0  | 100  | 0.3515          | 0.875    |
| 0.4014        | 11.0  | 110  | 0.3110          | 0.8969   |
| 0.3896        | 12.0  | 120  | 0.3030          | 0.9031   |
| 0.3822        | 13.0  | 130  | 0.3473          | 0.8812   |
| 0.3985        | 14.0  | 140  | 0.3288          | 0.8875   |
| 0.3826        | 15.0  | 150  | 0.2925          | 0.9      |
| 0.3716        | 16.0  | 160  | 0.3619          | 0.875    |
| 0.365         | 17.0  | 170  | 0.2941          | 0.9      |
| 0.3379        | 18.0  | 180  | 0.3239          | 0.8844   |
| 0.3365        | 19.0  | 190  | 0.3260          | 0.8906   |
| 0.3429        | 20.0  | 200  | 0.3096          | 0.8938   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1